Python for Data Engineering Project from edX

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Journey into the realm of becoming a Data Engineer and apply your basic Python knowledge of working with data. You will exercise various techniques in Python to extract data in multiple file formats from different sources, transform it into specific datatypes, and then prepare it for loading it into a database. You will perform these tasks with the help of multiple hands-on labs using Jupyter notebooks and IBM Watson Studio.

On completion of this course, you will have the confidence to employ Python for data engineering tasks such as extracting large data sets from multiple sources through the use of webscraping and APIs, transforming the data and making it ready for gaining valuable business insights.

NOTE: This course is not intended to teach you Python basics and has limited instructional content. Rather, it is intended for you to apply prior Python knowledge.

PRE-REQUISITE: The Python Basics for Data Science course from IBM is a pre-requisite for this project course. Before taking this course, please ensure that you have either completed the Python Basics for Data Science from IBM or have equivalent proficiency in working with Python and data.

What's inside

Learning objectives

Perform webscraping and data extraction using apis
Transform data into specific data types

Log operations and prepare data for loading
Perform etl tasks using python and jupyter notebooks

Perform webscraping and data extraction using apis
Transform data into specific data types
Log operations and prepare data for loading
Perform etl tasks using python and jupyter notebooks

Syllabus

Module 1: Python Project for Data Engineering

****Collect data using APIs and Webscraping

Extract data from different file formats

Transform data and prepare for loading

Log data operations

Share your Jupyter notebook in Watson Studio

Submit work and review your peers

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Emphasizes practical Python application in data engineering, such as webscraping and API integration

Provides a strong foundation for learners with basic Python knowledge who seek to advance their data engineering skills

Leverages hands-on labs and interactive materials through Jupyter notebooks and IBM Watson Studio for practical learning

Requires prior Python proficiency, making it suitable for learners with a foundation in Python

Assumes learners have completed the Python Basics for Data Science course from IBM

Focuses on practical Python application rather than theoretical concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Data Engineering Project with these activities:

Evaluate and Critique Peer Work

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Gain valuable perspectives by evaluating and providing feedback on peer work, fostering a collaborative learning environment and improving your own critical thinking skills.

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Review the peer work thoroughly.
Identify strengths and weaknesses in the work.
Provide constructive feedback to help improve the work.
Be receptive to feedback from others on your own work.
Use evaluation criteria to guide your feedback.

Share Jupyter Notebooks on Watson Studio

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Become familiar with sharing Jupyter notebooks on Watson Studio to facilitate collaboration and showcase your data engineering projects.

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Create a Watson Studio account.
Upload your Jupyter notebook to Watson Studio.
Share your notebook with collaborators.
Use Watson Studio features to enhance notebook sharing and collaboration.
Review notebook sharing best practices.

Extract Data Using APIs and Webscraping

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Practice using Python to extract data from multiple sources, such as APIs and websites, to strengthen your data engineering skills.

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Set up a Python environment and install necessary libraries.
Find and explore different APIs and websites that提供data available for extraction.
Write Python code to extract data from the identified sources.
Clean and process the extracted data to ensure its accuracy and completeness.
Save the extracted data in a structured format, such as a CSV file or database.

Four other activities

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Practice Data Logging for Data Engineering

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Develop skills in logging operations and data lineage to enhance your ability to track and debug data engineering processes.

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Understand the importance of data logging in data engineering.
Implement logging mechanisms in Python using libraries and frameworks.
Configure logging levels and formats for different data engineering operations.
Retrieve and analyze log data to identify errors and bottlenecks.
Use logging to maintain data lineage and track data transformations.

Transform Data into Various Formats

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Develop proficiency in transforming data into specific data types and formats to enhance your ability to prepare data for storage and analysis.

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Review data types and data formats.
Use Python functions and libraries to convert data between different types and formats.
Practice data normalization and standardization techniques.
Save the transformed data in a suitable format.
Validate the transformed data for accuracy and consistency.

Prepare Data for Loading into a Database

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Acquire hands-on experience in preparing data for loading into a database, ensuring its integrity and compatibility.

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Understand the database schema and data types.
Clean and validate the data to meet the database requirements.
Map the data to the appropriate database columns and tables.
Use Python functions and tools to generate SQL statements for data insertion.
Test the data loading process to verify accuracy.

Perform ETL Operations Using Python

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Gain practical experience in performing end-to-end ETL operations using Python, solidifying your understanding of data engineering best practices.

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Define the data extraction, transformation, and loading processes.
Write Python code to implement the ETL pipeline.
Use Python libraries and functions for data extraction, transformation, and loading.
Test and validate the ETL pipeline's functionality and performance.
Optimize the ETL pipeline for efficiency and scalability.

Career center

Learners who complete Python for Data Engineering Project will develop knowledge and skills that may be useful to these careers:

Data Engineer

As a Data Engineer, you would design and build data pipelines. This course can aid you by helping you extract data from multiple sources, transform it into specific data types, and prepare it for loading into a database. The course is hands-on and uses Jupyter notebooks, which are commonly used by Data Engineers.

See salaries and explore the career path for Data Engineer

Data Analyst

As a Data Analyst, you must prepare data for analysis. Knowing how to perform ETL tasks using Python can give you a leg up on other applicants. This course can help you extract and transform data using Python and Jupyter Notebooks.

See salaries and explore the career path for Data Analyst

Web Developer

As a web developer, you would make use of several technologies including Python. This course can help you use Python to extract data from websites, which is a common task for web developers.

See salaries and explore the career path for Web Developer

Data Scientist

As a Data Scientist, you must have a strong understanding of data and how to prepare it for analysis. This course can help you extract and transform data using Python and Jupyter Notebooks, which are commonly used by Data Scientists.

See salaries and explore the career path for Data Scientist

Software Engineer

As a Software Engineer, you could work on projects that involve data engineering. This course can help you build a foundation in Python for data engineering, which may be helpful for these projects.

See salaries and explore the career path for Software Engineer

Business Analyst

As a Business Analyst, you may need to work with data to solve business problems. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Business Analyst

Product Manager

As a Product Manager, you may need to work with data to understand your users and make decisions about your product. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Product Manager

Consultant

As a Consultant, you may need to work with data to solve problems for your clients. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Consultant

Market Researcher

As a Market Researcher, you may need to work with data to understand your market and make decisions about your product or service. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Market Researcher

Financial Analyst

As a Financial Analyst, you may need to work with data to analyze financial performance and make investment decisions. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

As an Operations Research Analyst, you may need to work with data to solve operational problems and improve efficiency. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Operations Research Analyst

Statistician

As a Statistician, you may need to work with data to analyze data and draw conclusions. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Statistician

Actuary

As an Actuary, you may need to work with data to assess risk and make financial decisions. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Actuary

Data Warehouse Manager

As a Data Warehouse Manager, you may need to work with data to design and manage data warehouses. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Data Warehouse Manager

Database Administrator

As a Database Administrator, you may need to work with data to design and manage databases. This course can help you extract and transform data using Python and Jupyter Notebooks, which may be helpful for these tasks.

See salaries and explore the career path for Database Administrator